Denoising camera data - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 3

2 Camera noise
To apply denoising on raw data, we first need a realistic model for the camera noise. There-
fore, we measure the real camera noise in the raw domain based on a series of exposures and
calculate the noise variance using the photon transfer method [ 11 ] . While this measurement
can be performed with any camera, we use the ARRI Alexa camera, as it delivers uncom-
pressed raw data in 16 bit precision. Since the data is uncompressed, we can expect unaltered
measurement results and additionally the individual camera processing steps are known for
this camera [ 10 ] . Our method can equivalently be used for other cameras.
The Alexa camera has been developed for motion picture recordings in digital cinema ap-
plications. It has a CMOS sensor with a resolution of 2880 × 1620. In front of the sensor, the
camera has a filter pack composed of an infrared cut-off filter, an ultraviolet cut-off filter and
an optical low-pass filter to reduce aliasing. The CFA, which is located between the filter pack
and the sensor, is a Bayer mask.
The photon transfer method [ 11 ] uses two subsequent frames recorded at constant and ho-
mogenous lighting conditions. The noise variance is calculated as the mean of the difference
between these two frames, the corresponding signal value is calculated as the mean over all
the signal values in these frames. The graph in Figure 1 (a) shows the variance ploted over the
respective mean signal value. The variance of the sensor noise can be approximated by a linear
model, which matches the results reported in Ref. [ 12 ] , where other cameras have been stud-
ied. We observe, however, one difference in the signal region around value 0.1 × 10 4 . The step
in the variance curve is due to a special characteristic of the Alexa sensor, the dual-gain read-
out technology. The sensor read-out of the Alexa provides two different paths with different
ampliication (dual-gain read-out). The low amplified path provides the data for the signal
range starting from 0.1 × 10 4 . The high amplified path saturates in the high signal values, but
for the low signal values it provides a significantly higher signal-to-noise ratio. The read-out
noise (offset of the variance curve) is reduced, thus the dual-gain technology enhances the low
light performance of the camera. The two read-out paths are combined in the region around
the signal value 0.1 × 10 4 , which explains the step in the variance curve.
FIGURE 1 Variance and distribution of the noise in the raw domain (signal values in 16 bit
precision).
The distribution is very similar to a Gaussian distribution. In Figure 1 (b) the distribution at
signal level 1265 is shown with the Gaussian approximation. The difference between the ap-
proximation and the measured histogram is small, thus we can well approximate the sensor
noise n in the raw domain using a Gaussian distribution with signal-dependent variance.
(1)